REU Program Fall 2025

Program Overview

Program Duration: September 29 - December 6, 2025

This document outlines the weekly milestones for our Research Experience for Undergraduates (REU) Computational Data Science program. Our cohort includes students working on two primary research tracks:

• Group 1 (Computer Vision): Marcus Jones, Sydney Johnson, Massai Morgan - Food Classification Application
• Group 2 (NLP): Seth Thomas, Breosha young - EU Taxonomy Question Answering System

Welcome to our Research Experience for Undergraduates CDS (Computational Data Science) program! Below you’ll find our talented cohort of undergraduate researchers, their academic backgrounds, and the exciting research projects they’re working on this Fall 2025.

Student Profiles

Marcus Jones

Marcus Jones

Major: Computer Science Year: Senior

Research Interest: Deep Learning Applications in Culinary Context (Group 1)

Marcus will develop a computer vision application that classifies a photographed food item and returns the predicted label with an associated confidence score. This tool supports reliable, on-device food recognition.

Email: marcus3.jones@famu.edu

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Seth Thomas

Seth Thomas

Major: Computer Science Year: Junior

Research Interest: Question Answering with Deep Learning (Group 2)

Thomas will develop an intelligent question-answering application that processes the European Union (EU) Taxonomy for sustainable activities to respond to user queries on sustainable finance. The system will extract and structure regulatory concepts from the EU Taxonomy, enabling automated, evidence-based answers to finance and sustainability questions..

Email: seth2.thomas@famu.edu

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Sydney Jhonson

Sydney Jhonson

Major: Computer Information Systems Year: Sophomore

Research Interest: Deep Learning Applications in Culinary Context (Group 1)

Sydney will develop a computer vision application that classifies a photographed food item and returns the predicted label with an associated confidence score. This tool supports reliable, on-device food recognition.

Email: sydney8.johnson@famu.edu

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Massai Morgan

Massai Morgan

Major: Computer Information Systems Year: Junior

Research Interest: Deep Learning Applications in Culinary Context (Group 1)

Morgan will develop a computer vision application that classifies a photographed food item and returns the predicted label with an associated confidence score. This tool supports reliable, on-device food recognition.

Email: sydney8.johnson@famu.edu

Weekly Milestones

Week 1: Program Orientation & Foundation Setting

Dates: September 29 - October 5, 2025

All Students

• Complete program orientation and safety training
• Set up development environments (Python, Git, IDE)
• Create GitHub repositories for individual projects
• Establish weekly meeting schedules with mentors
• Complete literature review assignment (5 relevant papers minimum)

Group 1 (Computer Vision - Food Classification)

• Marcus, Sydney, Massai: Research existing food classification datasets (Food-101, Recipe1M+, etc.)
• Install and configure computer vision libraries (OpenCV, PyTorch/TensorFlow)
• Complete basic image processing tutorial

Group 2 (NLP - Question Answering)

• Seth: Download and explore EU Taxonomy documentation
• Set up NLP development environment (transformers, spaCy, NLTK)
• Complete basic text processing and question-answering tutorial

Deliverable: Written summary of literature review findings (2-3 pages)

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Week 2: Data Exploration & Preprocessing

Dates: October 6 - October 12, 2025

All Students

• Present literature review findings to cohort
• Begin project proposal drafting
• Attend “Ethics in AI Research” workshop

Group 1 (Computer Vision)

• Marcus, Sydney, Massai: Download and explore chosen food dataset
• Implement basic data preprocessing pipeline (resize, normalize, augmentation)
• Create data visualization dashboard showing class distributions
• Test basic image loading and display functionality

Group 2 (NLP)

• Seth: Parse EU Taxonomy documents into structured format
• Implement text preprocessing pipeline (tokenization, cleaning, normalization)
• Create initial document indexing system
• Extract key regulatory concepts and definitions

Deliverable: Data exploration report with visualizations

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